Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

A STUDY ON DATA STREAMING IN FOG COMPUTING ENVIRONMENT

In lately years, data streaming is become more important day by day, considering technologies employed to servethat manner and share number of terminals within the system either direct or indirect interacting with them.Smart devices now play active role in the data streaming environment as well as fog and cloud compatibility. It is affectingthe data collectivity and appears clearly with the new technologies provided and the increase for the number of theusers of such systems. This is due to the number of the users and resources available system start to employ the computationalpower to the fog for moving the computational power to the network edge. It is adopted to connect system that streamed dataas an object. Those inter-connected objects are expected to be producing more significant data streams, which are produced atunique rates, in some cases for being analyzed nearly in real time. In the presented paper a survey of data streaming systemstechnologies is introduced. It clarified the main notions behind big data stream concepts as well as fog computing. From thepresented study, the industrial and research communities are capable of gaining information about requirements for creatingFog computing environment with a clearer view about managing resources in the Fog.The main objective of this paper is to provide short brief and information about Data Streaming in Fog ComputingEnvironment with explaining the major research field within this meaning

Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein